Field of view limits of image sensors

ABSTRACT

In some examples, an electronic device comprises an image sensor and a processor. The processor is to detect a feature of a frame of a video received via the image sensor, determine whether the feature indicates an object of interest, and, responsive to a determination that the feature indicates the object of interest, limit a first field of view of the image sensor to the object of interest and overlay the first field of view with a second field of view. The second field of view is unlimited.

BACKGROUND

Electronic devices such as notebooks, laptops, desktops, tablets, andsmartphones may include executable code that enables users to sharelivestreamed videos during virtual interactions (e.g., videoconferencingapplications, social media applications). A virtual interaction, as usedherein, may be any online event that allows a user of an electronicdevice to interact with users of other electronic devices via an imagesensor, an audio device, or a combination thereof. A shared livestreamedvideo may include an object of interest that the user wants to exhibitto the audience by presenting the object of interest to the imagesensor.

BRIEF DESCRIPTION OF THE DRAWINGS

Various examples are described below referring to the following figures.

FIG. 1 is a block diagram of an electronic device for limiting a fieldof view of an image sensor in accordance with various examples.

FIGS. 2A, 2B, and 2C are an example of an electronic device limiting afield of view of an image sensor in accordance with various examples.

FIG. 3 is a block diagram of an electronic device for limiting a fieldof view of an image sensor in accordance with various examples.

FIGS. 4A, 4B, and 4C are an example of an electronic device limiting afield of view of an image sensor in accordance with various examples.

FIG. 5 is a block diagram of an electronic device for limiting a fieldof view of an image sensor in accordance with various examples.

FIGS. 6A and 6B are an example of an electronic device limiting a fieldof view of an image sensor in accordance with various examples.

DETAILED DESCRIPTION

As described above, electronic devices such as notebooks, laptops,desktops, tablets, and smartphones may include executable code thatenables users to share objects of interest with an audience via imagesensors. An object of interest, as used herein, is a tangible item thatthe user exhibits to the audience. The object of interest may be a book,a phone, a framed picture, or a gadget, for instance. However, inaddition to the object of interest, the image sensor may capture othertangible items (e.g., the user, a piece of furniture, a wall decoration,a table decoration) in a field of view of the image sensor. A field ofview, as used herein, is an area that the image sensor detects given anorientation and a position of the image sensor within a physicalenvironment. Displaying tangible items other than the object of interestmay distract the user and the audience and disrupt the virtualinteraction.

This description describes an electronic device that analyzes a frame ofa livestream video received via an image sensor to determine an objectof interest and limits the field of view of the image sensor to theobject of interest. Limiting a field of view, as used herein, reduces anarea, or portion, of the physical environment, that the image sensor mayrecord for display to an audience. The livestream video is hereafterreferred to as “the video.” The electronic device decomposes a frame ofthe video to detect features of the frame. The frame of the video is astatic image taken from the video. Decomposing, as used herein, is toreduce the frame to its edge-like structures by removing fine-scaledetails of the tangible items of the frame. Features, as used herein,are the edge-like structures, or outlines, of the tangible items of theframe. The electronic device determines which of the features representsthe object of interest. The electronic device may prompt a user toverify a tangible item represented by the determined feature is theobject of interest. The electronic device may limit the field of view ofthe image sensor that is displayed to the audience to the object ofinterest. Utilizing the electronic device that enables sharing of avideo having a field of view limited to an object of interest providesfor an enhanced user and audience experience by focusing the user andthe audience attention on the object of interest.

In some examples in accordance with the present description, anelectronic device is provided. The electronic device includes an imagesensor and a processor. The processor is to detect a feature of a frameof a video received via the image sensor, determine whether the featureindicates an object of interest, and, responsive to a determination thatthe feature indicates the object of interest, limit a first field ofview of the image sensor to the object of interest and overlay the firstfield of view with a second field of view, the second field of viewunlimited.

In other examples in accordance with the present description, anelectronic device is provided. The electronic device includes an imagesensor and a processor. The processor is to detect multiple features ofa frame of a video received via the image sensor, determine that a firstsubset of the multiple features represents a first object and a secondsubset of the multiple features represents a second object, determinewhether the first object is an object of interest, and, based on adetermination that the first object is the object of interest, limit afield of view of the image sensor to the first object and exclude thesecond object.

In yet other examples in accordance with the present description, anon-transitory machine-readable medium is provided. The non-transitorymachine-readable medium includes all electronic mediums or media ofstorage, except signals. The non-transitory machine-readable mediumstores machine-readable instructions. When executed by a processor of anelectronic device, the machine-readable instructions cause the processorto detect a user gesture in a video signal received via the imagesensor, determine that the user gesture indicates that the video signalcomprises an object of interest, detect multiple features of a frame ofthe video signal, determine, utilizing a machine learning technique, asubset of features of the multiple features indicates the object ofinterest, and limit a field of view of the image sensor to the subset offeatures.

Referring now to FIG. 1 , an electronic device 100 for limiting a fieldof view of an image sensor 106 is provided, in accordance with variousexamples. The electronic device 100 may be a notebook, a laptop, adesktop, a tablet, a smartphone, or any other suitable computing deviceincluding executable code that enables users to share videos duringvirtual interactions. The electronic device 100 may include a chassis102. The chassis 102 may house hardware components of the electronicdevice 100 such as a display panel 104, the image sensor 106, aprocessor 108, a wireless transceiver 110, and a storage device 112. Thechassis 102 may be plastic, glass, glass coated with a conductivematerial, or a combination thereof. The display panel 104 and the imagesensor 106 may be housed on an outer surface of the chassis 102. Theprocessor 108, the wireless transceiver 110, and the storage device 112are housed within the chassis 102. The display panel 104 may be a liquidcrystal display (LCD) panel, a light-emitting diode (LED) display panel,a plasma display panel, a quantum dot (QD) display panel, or anysuitable panel for displaying data of the electronic device 100 forviewing. The image sensor 106 may be any suitable device for generatinga video signal. The processor 108 may be a microprocessor, amicrocomputer, a microcontroller, a programmable integrated circuit, aprogrammable gate array, or another suitable device for managingoperations of the electronic device 100. The wireless transceiver 110 isto transmit and receive wireless signals. The wireless transceiver 110may transmit and receive a BLUETOOTH® signal, a WI-FI® signal, or acombination thereof, for example. The storage device 112 may include ahard drive, solid state drive (SSD), flash memory, random access memory(RAM), or other suitable memory for storing data or executable code ofthe electronic device 100.

In some examples, the processor 108 couples to the display panel 104(connection not explicitly shown), the image sensor 106, the wirelesstransceiver 110, and the storage device 112. The storage device 112 maystore machine-readable instructions which, when executed by theprocessor 108, cause the processor 108 to perform some or all of theactions attributed herein to the processor 108. The machine-readableinstructions may be the machine-readable instructions 114, 116, 118.

In various examples, when executed by the processor 108, themachine-readable instructions 114, 116, 118 cause the processor 108 tolimit the field of view of the image sensor 106. The machine-readableinstruction 114 causes the processor 108 to detect a feature of a frameof a video. The processor 108 may receive the video via the image sensor106. The machine-readable instruction 116 causes the processor 108 todetermine whether the feature indicates an object of interest.Responsive to a determination that the feature indicates the object ofinterest, the machine-readable instruction 118 causes the processor 108to overlay a first field of view of the image sensor 106 with a secondfield of view of the image sensor 106.

As described above, in some examples, the electronic device 100 analyzesa frame of a video received via the image sensor 106 to determine anobject of interest and limits the field of view of the image sensor 106to the object of interest. The frame of the video may be an image of auser holding a tangible item, for example. The machine-readableinstruction 114 may cause the processor 108 to decompose a frame of thevideo to detect the feature of the frame, for example. The processor 108may decompose the frame utilizing image pre-processing techniques. Theimage pre-processing techniques may include grayscaling, blurring,thresholding, dilating, erosion, or a combination thereof. For example,the processor 108 may convert the frame to a grayscale image. Thegrayscale image has color removed to enhance the feature of the frame.The processor 108 may blur the grayscale image to remove noise from thegrayscale image. The processor 108 may threshold the blurred image toconvert the blurred image into black and white pixels. The processor 108may determine that the white pixels indicate the feature of the frame.In various examples, the processor 108 may dilate (e.g., add pixels to),erode (e.g., remove pixels from), or a combination thereof thethresholded image to enhance the feature of the frame.

The processor 108 may determine whether the feature indicates the objectof interest by prompting a user to verify that the tangible itemrepresented by the feature indicates the object of interest, performinga feature comparison, or a combination thereof. For example, thedecomposed frame may include the feature, and the machine-readableinstruction 116 causes the processor 108 to determine whether thefeature indicates the object of interest by prompting the user to verifythat the tangible item represented by the feature indicates the objectof interest. The processor 108 may prompt the user to verify that thetangible item represented by the feature indicates the object ofinterest by causing the display panel 104 to display a field of view ofthe image sensor 106 limited to the tangible object represented by thefeature.

In other examples, the machine-readable instruction 116 causes theprocessor 108 to determine whether the feature indicates the object ofinterest by performing a feature comparison. The feature comparison maycompare a location of the feature relative to a central axis of theimage sensor 106, a dimension of the feature to a dimension of the fieldof view of the image sensor 106, or a combination thereof. The centralaxis of the image sensor 106, as used herein, is an imaginary linethrough the center of curvature of a lens of the image sensor 106.

For example, the processor 108 may determine the location of the featurerelative to the central axis of the image sensor 106 by determiningwhether the feature is within a specified area of the central axis ofthe image sensor 106. The specified area may be demarcated by an anglethat radiates outward from the image sensor 106, by an orthogonalcoordinate system having the central axis of the image sensor 106 as anaxis of the orthogonal coordinate system, or a combination thereof, asdescribed below with respect to FIG. 2B. The processor 108 may determinethat because the location of the feature indicates that the user ispresenting the tangible item represented by the feature with thespecified area the feature indicates the object of interest.

In another example, the processor 108 may compare the dimension of thefeature to the dimension of the field of view of the image sensor 106.The processor 108 may determine that the dimension of the featurerelative to the dimension of the field of view indicates that the useris presenting the tangible item represented by the feature in closeproximity to the image sensor 106. For example, the processor 108 maydetermine that the feature indicates the object of interest because thedimension of the feature exceeds twenty-five percent of the dimension ofthe field of view.

Responsive to the processor 108 determining that the feature indicatesthe object of interest, the machine-readable instruction 118 causes theprocessor 108 to overlay a first field of view of the image sensor 106with a second field of view of the image sensor 106. In some examples,the processor 108 limits a first field of view of the image sensor 106to the object of interest and overlays the first field of view with asecond field of view. The second field of view is an unlimited field ofview of the image sensor 106. In some examples, the processor 108receives the video via the image sensor 106. The video is a video of theunlimited field of view of the image sensor 106. The processor 108modifies the video so that the limited field of view is overlaid by theunlimited field of view. The processor 108 may cause the display panel104 to display the limited field of view overlaid with the unlimitedfield of view. In other examples, the processor 108 may cause thewireless transceiver 110 to transmit the video comprising the limitedfield of view overlaid with the unlimited field of view. The limitedfield of view of the image sensor 106 overlaid with the unlimited fieldof view of the image sensor 106 may be referred to herein as a“picture-in-picture video.” A dimension of the unlimited field of viewthat is displayed or transmitted may be a percentage of the limitedfield of view. For example, the dimension of the unlimited field of viewmay be a tenth to a quarter of the dimension of the limited field ofview. The unlimited field of view may be located in a specified zone ofthe limited field of view, as described below with respect to FIG. 2C.

In other examples, the processor 108 the first field of view is anunlimited field of view of the image sensor 106 and the second field ofview is the field of view of the image sensor 106 limited to the objectof interest. The processor 108 modifies the video of the unlimited fieldof view so that the unlimited field of view is overlaid by the limitedfield of view. The processor 108 may cause the display panel 104 todisplay the unlimited field of view overlaid with the limited field ofview. In other examples, the processor 108 may cause the wirelesstransceiver 110 to transmit the video comprising the unlimited field ofview overlaid with the limited field of view. A dimension of the limitedfield of view that is displayed or transmitted may be a percentage ofthe unlimited field of view. For example, the dimension of the limitedfield of view may be a tenth to a quarter of the dimension of theunlimited field of view. The limited field of view may be located in aspecified zone of the unlimited field of view. In various examples, theuser may determine whether the picture-in-picture video is the limitedfield of view overlaid with the unlimited field of view or the unlimitedfield of view overlaid with the limited field of view.

While not explicitly shown, the electronic device 100 may also include avideo adapter, a sound card, a network card, local buses, input/outputdevices (e.g., a microphone, a speaker, a mouse, a keyboard, atouchpad), or a combination thereof. While the display panel 104 isshown as an integrated display panel 104 of the electronic device 100,in other examples, the display panel 104 may be a display panel 104 of adisplay device that is coupled to the electronic device 100 via a wiredconnection (e.g., Universal Serial Bus (USB), Video Graphics Array(VGA), Digital Visual Interface (DVI), High-Definition MultimediaInterface (HDMI)) or via a wireless connection to the wirelesstransceiver 110. In some examples, the display panel 104 may be aflexible display panel. A flexible display panel, as used herein, is adisplay panel that may be deformed (e.g., rolled, folded, etc.) within agiven parameter or specification (e.g., a minimum radius of curvature)without losing electrical function or connectivity. While the imagesensor 106 is shown as an internal camera, in other examples, the imagesensor 106 may couple to the processor 108 via a wired connection (e.g.,USB) or via a wireless connection to the wireless transceiver 110.

Referring now to FIGS. 2A, 2B, and 2C, an example of an electronicdevice (e.g., the electronic device 100) limiting a field of view of animage sensor (e.g., the image sensor 106) is provided, in accordancewith various examples. FIG. 2A includes the electronic device displayingan image 200. The image 200 is an unlimited field of view of the imagesensor. The image 200 includes a user hand held in a gesture 202 and atangible item 204. The tangible item 204 may be a Mother's Day card or aMother's Day decor item, for example. FIG. 2B includes a decomposedimage 208. The decomposed image 208 is a decomposition of the image 200.The decomposed image 208 includes a feature 210, a central axis 212 ofthe feature 210, a point 214, a first axis 216, a second axis 218, aboundary 220, and a zone 222. The feature 210 represents the tangibleitem 204. The point 214 represents the central axis of the image sensor.The first axis 216 and the second axis 218 may be axes of an orthogonalsystem having the central axis of the image sensor as a third axis. Theboundary 220 represents an angle that radiates outward from the imagesensor and intersects the feature 210. The zone 222 represents an areawithin the orthogonal system. FIG. 2C includes an image 224. The image224 includes an image 226, the user hand held in a gesture 228, and atangible item 230. The image 226 may be the image 200. The gesture 228may be the gesture 202. The tangible item 230 may be the tangible item204.

As described above, in various examples, a processor (e.g., theprocessor 108) of the electronic device detects a feature of a frame ofa video. For example, the frame may be the image 200 of FIG. 2A. Theprocessor may decompose the image 200 to detect the feature. As shown inFIG. 2B, the decomposed image 208 includes the feature 210, which is anoutline of the tangible item 204. The processor determines whether thefeature 210 indicates the object of interest by prompting a user of theelectronic device to verify that the tangible item 204 is the object ofinterest, by comparing a location of the feature 210 relative to thepoint 214, by comparing a dimension of the feature 210 to the dimensionof a field of view of the image sensor, or a combination thereof. Forexample, the processor may determine that the feature 210 intersects theboundary 220 that represents a twelve-degree angle that radiates outwardfrom the image sensor, a proportion of the dimension of the feature 210to the dimension of the field of view is twenty-five percent, or acombination thereof. Based on the angle having a value that is less thanor equal to thirty degrees, the proportion having a value that isgreater than twenty percent, or a combination thereof, the processor mayprompt the user to verify that the tangible item 204 represented by thefeature 210 is the object of interest. Responsive to a determinationthat the feature 210 indicates the object of interest, the processorlimits the field of view of the image sensor, as shown in FIG. 2C, tothe image 224 to emphasize the tangible item 230 and overlays the image224 with the image 226. The image 226 is the unlimited field of view ofthe image sensor (e.g., the image 200 of FIG. 2A). The image 226 may belocated in the zone 222 of the image 224. By providing the limited fieldof view of the image sensor, the electronic device may enlarge aperspective of the object of interest to enhance a demonstration of afunctionality of the object of interest, a particular view of the objectof interest, or a combination thereof, thereby enhancing the user andaudience experience.

In some examples, the processor may perform post-processing techniquesto reorient the limited field of view of FIG. 2C. The post-processingtechniques may include warping a perspective, adjusting a resolution, ora combination thereof. In various examples, the processor may warp theperspective of an image including an object of interest so that an angleof the central axis of the object of interest aligns with an axis of theorthogonal system of a decomposed frame. As shown in FIG. 2C, theprocessor reorients the image 224 so that the central axis of thetangible item 230 (e.g., the central axis 212 of the feature 210) isparallel to the first axis 216 of FIG. 2B. In other examples, theprocessor may adjust a resolution of the video to enhance a quality ofthe video. For example, the processor may apply super resolution to thelimited field of view, which is a cropped image of the unlimited fieldof view, so that the limited field of view is similar in quality to aresolution of the unlimited field of view. In various examples, theimage 226 is located in other zones of the image 224 to reduce overlapwith the object of interest. By adjusting an orientation of the image224, a size of the image 224, a location of the image 226, or acombination thereof, the processor may enhance a readability of theobject of interest, thereby enhancing the user and audience experience.

While the boundary 220 is shown centered around the point 214, in otherexamples, the boundary 220 may be located in other areas of the field ofview of the image sensor. For example, the processor may utilize amachine learning technique to determine that the user exhibits objectsof interest in an upper left quadrant of the orthogonal system of FIG.2B. The processor may determine the boundary 220 is located in the upperleft quadrant of the orthogonal system of FIG. 2B. While the examplesabove utilize the orthogonal system of FIG. 2B to analyze the frame, inother examples, the processor may utilize a different system foranalyzing three-dimensional (3D) objects of a video in a two-dimensional(2D) frame of the video.

Referring now to FIG. 3 , an electronic device 300 for limiting a fieldof view of an image sensor (e.g., the image sensor 106) is provided, inaccordance with various examples. The electronic device 300 may be theelectronic device 100. The electronic device 300 may include a chassis302. The chassis 302 may be the chassis 102. The chassis 302 may have anouter surface 304. The outer surface 304 may provide access to aconnector 306. The connector 306 may be any suitable connector forcoupling to the image sensor. For example, the connector 306 may be aUSB connector. The chassis 302 may house hardware components of theelectronic device 300 such as a processor 308, a wireless transceiver310, and a storage device 312. The processor 308 may be the processor108. The wireless transceiver 310 may be the wireless transceiver 110.The storage device 312 may be the storage device 112.

In some examples, the processor 308 couples to the connector 306, thewireless transceiver 310, and the storage device 312. The storage device312 may store machine-readable instructions which, when executed by theprocessor 308, cause the processor 308 to perform some or all of theactions attributed herein to the processor 308. The machine-readableinstructions may be the machine-readable instructions 314, 316, 318,320.

In various examples, when executed by the processor 308, themachine-readable instructions 314, 316, 318, 320 cause the processor 308to limit the field of view of the image sensor to an object of interest.The image sensor may couple to the connector 306 or wirelessly couple toa wireless transceiver 310. The machine-readable instruction 314 causesthe processor 308 to detect multiple features of a frame of a video. Theprocessor 308 may receive the video via the image sensor. Themachine-readable instruction 316 causes the processor 308 to determinewhich subset of features of the multiple features represent a firsttangible item, or a first object, and a second tangible item, or asecond object. The machine-readable instruction 318 causes the processor308 to determine whether the first object is the object of interest. Themachine-readable instruction 320 causes the processor 308 to, based onthe determination that the first object is the object of interest, limita field of view of the image sensor.

As described above, in some examples, the electronic device 300 analyzesa frame of a video received via the image sensor to determine the objectof interest and limits the field of view of the image sensor to theobject of interest. The machine-readable instruction 314 may cause theprocessor 308 to decompose a frame of the video to detect the multiplefeatures of the frame. As described above, the processor 308 maydecompose the frame utilizing image pre-processing techniques such asgrayscaling, blurring, thresholding, dilating, erosion, or a combinationthereof. Responsive to the decomposed frame including multiple features,the machine-readable instruction 316 may cause the processor 308 todetermine that a first subset of the multiple features represents thefirst object and a second subset of the multiple features represents thesecond object by identifying connected subsets of the multiple features.A connected subset of the multiple features, as used herein, is a groupof pixels of the decomposed frame having a same value (e.g., a samecolor) and that touch in a contiguous manner to form an outline.

The machine-readable instruction 318 may cause the processor 308 todetermine whether the first object is the object of interest byperforming a feature comparison, a frame-by-frame feature comparison, ora combination thereof. The feature comparison may compare a location ofthe first object relative to a central axis of the image sensor and alocation of the second object relative to the central axis of the imagesensor, a dimension of the first object to a dimension of the secondobject, the location of the first object relative to the location of thesecond object, or a combination thereof.

For example, the processor 308 may determine the location of the firstsubset of the multiple features relative to the central axis of theimage sensor by determining a distance from a center point of the firstsubset of the multiple features and the central axis of the imagesensor. The processor 308 may determine distances for multiple subsetsof the multiple features. The processor 308 may determine that adistance of the first subset of the multiple features indicates that theuser is presenting the tangible item represented by the first subset ofthe multiple features nearest to the central axis of the image sensor.The processor 308 may prompt the user to verify that the tangible itemrepresented by the first subset of the multiple features indicates theobject of interest by causing a display panel (e.g., the display panel104) to display a field of view of the image sensor limited to thetangible item represented by the first subset of the multiple features.

In some examples, the machine-readable instruction 318 causes theprocessor 308 to determine whether the first subset of the multiplefeatures indicates the object of interest by performing a frame-by-framefeature comparison. Performing the frame-by-frame feature comparison,the processor 308 utilizes feature comparison to compare multiplesubsets of the multiple features between multiple frames. In variousexamples, the processor 308 may determine that a feature is changingpositions between frames. The processor 308 may determine thatstationary features are background objects in the image and that thefeature that is changing positions represents the object of interest. Inother examples, the processor 308 may determine that a first featurethat is present in sequential frames is the object of interest because asecond feature that is present in a first frame of the sequential framesis absent from a second frame of the sequential frames. In someexamples, the machine-readable instruction 320 causes the processor 308to, based on the determination that the first object is the object ofinterest, limit a field of view of the image sensor to the first objectand exclude the second object.

Referring now to FIGS. 4A, 4B, and 4C, an example of an electronicdevice (e.g., the electronic device 100, 300) limiting a field of viewof an image sensor (e.g., the image sensor 106, an image sensor coupledto the connector 306, an image sensor wirelessly coupled to the wirelesstransceiver 110, 310) is provided, in accordance with various examples.FIG. 4A includes the image 400. The image 400 includes a user hand heldin a gesture 402, a wrist ornament 404, and a tangible item 406. Thegesture 402 may be the gesture 202, 228. The wrist ornament 404 may be awatch or a bracelet. The tangible item 406 may be the tangible item 204,230. FIG. 4B includes an image 408. The image 408 includes a firstfeature 410, a second feature 412, a point 414, and a boundary 416. Thefirst feature 410 may be the feature 210. The point 414 may be the point214. The boundary 416 may be the boundary 220. FIG. 4C includes theimage 418. The image 418 includes a user hand held in a gesture 420 anda tangible item 422. The gesture 420 may be the gesture 202, 228, 402.The tangible item 422 may be the tangible item 204, 230, 406.

As described above with respect to FIG. 3 , a processor (e.g., theprocessor 108, 308) may determine whether the first feature 410 is theobject of interest by performing a feature comparison, a frame-by-framefeature comparison, or a combination thereof. The feature comparison maycompare a location of the first feature 410 relative to the boundary 416and a location of the second feature 412 relative to the boundary 416, adimension of the first feature 410 to a dimension of the second feature412, the location of the first feature 410 relative to the location ofthe second feature 412, or a combination thereof. For example, theprocessor may determine that the first feature 410 is the object ofinterest because the first feature 410 is located nearer to the point414 than the second feature 412, because the first feature 410 islocated within the boundary 416 and the second feature 412 is not withinthe boundary 416, because the first feature 410 encompasses a largerarea of the field of view than the second feature 412, or somecombination thereof. In another example, the processor may determinethat the first feature 410 is the object of interest because the secondfeature 412 is not present in a subsequent sequential frame, because thefirst feature 410 is located in a different position but still withinthe boundary 416, or some combination thereof.

Referring now to FIG. 5 , an electronic device 500 for limiting a fieldof view of an image sensor (e.g., the image sensor 106) is provided, inaccordance with various examples. The electronic device 500 may be theelectronic device 100, 300. The electronic device 500 includes aprocessor 502 and a non-transitory machine-readable medium 504. Theprocessor 502 may be the processor 108, 308. The non-transitorymachine-readable medium 504 may be the storage device 112, 312. Asdescribed above, the term “non-transitory” does not encompass transitorypropagating signals.

In various examples, the processor 502 couples to the non-transitorymachine-readable medium 504. The non-transitory machine-readable medium504 may store machine-readable instructions. The machine-readableinstructions may be the machine-readable instructions 506, 508, 510,512, 514. The machine-readable instructions 506, 508, 510, 512, 514,when executed by the processor 502, may cause the processor 502 toperform some or all of the actions attributed herein to the processor502.

In various examples, when executed by the processor 502, themachine-readable instructions 506, 508, 510, 512, 514 cause theprocessor 502 to limit a field of view of the image sensor. The imagesensor may be the image sensor 106, an image sensor wirelessly coupledto the wireless transceiver 110, 310, or an image sensor coupled to theconnector 306. The machine-readable instruction 506 causes the processor502 to detect a user gesture (e.g., the gesture 202, 228, 402, 420) in avideo signal. The processor 502 may receive the video signal via theimage sensor. The machine-readable instruction 508 causes the processor502 to determine that the user gesture indicates that the video signalincludes an object of interest. The machine-readable instruction 510causes the processor 502 to detect multiple features of a frame of thevideo signal. The machine-readable instruction 512 causes the processor502 to determine a subset of features of the multiple features indicatesthe object of interest. The machine-readable instruction 514 causes theprocessor 502 to limit the field of view of the image sensor to thesubset of features.

In various examples, the processor 502 may detect the multiple features,determine that the user gesture indicates that the video signal includesthe object of interest, determine that the subset of features indicatesthe object of interest, or a combination thereof by utilizing a machinelearning technique. In some examples, the processor 502 may utilize aconvolutional neural network (CNN) to detect the multiple features. Forexample, the processor 502 may utilize a region-based CNN (R-CNN). Theprocessor 502 may divide the frame of the video signal into multipleregions. In various examples, the processor 502 may utilize a RegionProposal Network (RPN) to determine the multiple regions. The processor502 inputs each region into the R-CNN. The processor 502 utilizes asupport vector machine technique to determine whether the outputs of theR-CNN include tangible objects. In another example, the processor 502may utilize a Fast R-CNN. The processor 502 may decompose the frame andthen utilizes the decomposed image (e.g., the image 208, 408) as aninput into the Fast R-CNN to detect the multiple features.

In some examples, the processor 502 may determine that the subset offeatures indicates the object of interest by utilizing a machinelearning technique to perform object tracking. For example, theprocessor 502 may utilize a CNN to compare frames having sequentialsequences and distinguish background objects from possible objects ofinterest. In another example, the processor 502 may utilize a CNN toperform a semantic segmentation technique that divides the frame intopixel groupings. The processor 502 utilizes the CNN to identify tangibleitems of the pixel groupings and features of the tangible items. Theprocessor 502 may utilize the machine learning technique to removesubsets of features of the multiple features that represent backgroundobjects. The processor 502 may determine whether a subset of featuresthat remain after removal of the background objects includes the objectof interest by determining a proximity of the subset of features to theuser. For example, the processor 502 may determine that the subset offeatures that is nearest the user represents the object of interest.

In other examples, the processor 502 may provide inputs to a machinelearning technique that include user gestures that indicated previousvideo signals included objects of interest, locations of backgroundobjects, verbal cues, or a combination thereof. The processor 502 maymonitor an audio signal embedded in the video signal for the verbal cue.Utilizing the machine learning technique, the processor 502 maydetermine that the user gesture that indicates that the video signalincludes the object of interest is a position of a finger, a position ofa hand, a movement of the finger, a movement of the hand, or acombination thereof. For example, the processor 502 may determine thatthe user points to an object of interest in a previous video signal andmay monitor the video signal for the user pointing. In another example,the processor 502 may determine that a user holds an object of interestin the palm of the user's hand and says, “Look at this,” in anotherprevious video signal and may monitor for the user making the usergesture, saying “Look at this,” or some combination thereof.

Referring now to FIGS. 6A and 6B, an example of an electronic device(e.g., the electronic device 100, 300, 500) limiting a field of view ofan image sensor (e.g., the image sensor 106, an image sensor coupled tothe connector 306, an image sensor wirelessly coupled to the wirelesstransceiver 110, 310) is provided, in accordance with various examples.FIG. 6A includes an image 600. The image 600 includes a user hand heldin a gesture 602, a wrist ornament 604, and a tangible item 606. Thegesture 602 may be the gesture 202, 228, 402, 420. The wrist ornament604 may be the wrist ornament 404. The tangible item 606 may be thetangible item 204, 230, 406, 422. FIG. 6B includes an image 608. Theimage 608 may be a field of view of the image sensor that is limited toan object of interest.

As described above with respect to FIG. 5 , utilizing the machinelearning technique, a processor (e.g., the processor 108, 308, 502) maydetermine that the gesture 602 indicates that the video signal includesthe object of interest. The processor may decompose a frame of the videosignal. The decomposed frame may include features for the wrist ornament604 and the tangible item 606. The processor may utilize the machinelearning technique to determine that the feature representing thetangible item 606 represents the object of interest because the tangibleitem 606 is located in the palm of the user's hand, because the featurerepresenting the wrist ornament 604 is a background object, or acombination thereof. The processor may utilize a post-processingtechnique to remove a background of the image 600 of FIG. 6A to limitthe field of view of the image sensor to the image 608. Removing thebackground of the video signal having a field of view limited to anobject of interest provides for an enhanced user and audience experienceby focusing the user and the audience attention on the object ofinterest.

The above description is meant to be illustrative of the principles andvarious examples of the present description. Numerous variations andmodifications become apparent to those skilled in the art once the abovedescription is fully appreciated. It is intended that the followingclaims be interpreted to embrace all such variations and modifications.

In the figures, certain features and components disclosed herein may beshown in exaggerated scale or in somewhat schematic form, and somedetails of certain elements may not be shown in the interest of clarityand conciseness. In some of the figures, in order to improve clarity andconciseness, a component or an aspect of a component may be omitted.

In the above description and in the claims, the term “comprising” isused in an open-ended fashion, and thus should be interpreted to mean“including, but not limited to . . . .” Also, the term “couple” or“couples” is intended to be broad enough to encompass both direct andindirect connections. Thus, if a first device couples to a seconddevice, that connection may be through a direct connection or through anindirect connection via other devices, components, and connections.Additionally, as used herein, the word “or” is used in an inclusivemanner. For example, “A or B” means any of the following: “A” alone, “B”alone, or both “A” and “B.”

What is claimed is:
 1. An electronic device, comprising: an imagesensor; and a processor to: detect a feature of a frame of a videoreceived via the image sensor; determine whether the feature indicatesan object of interest by prompting a user to verify that the featureindicates the object of interest, performing a feature comparison, or acombination thereof, wherein performing the feature comparison comprisescomparing a location of the feature relative to a central axis of theimage sensor, a dimension of the feature to a dimension of a secondfield of view, or a combination thereof; and responsive to adetermination that the feature indicates the object of interest, limit afirst field of view of the image sensor to the object of interest andoverlay the first field of view with the second field of view, thesecond field of view unlimited.
 2. The electronic device of claim 1,wherein the processor is to decompose the frame utilizing imagepre-processing techniques to detect the feature.
 3. The electronicdevice of claim 2, wherein the image pre-processing techniques includegrayscaling, blurring, thresholding, dilating, erosion, or a combinationthereof.
 4. An electronic device, comprising: an image sensor; and aprocessor to: detect multiple features of a frame of a video receivedvia the image sensor; determine that a first subset of the multiplefeatures represents a first object and a second subset of the multiplefeatures represents a second object; determine whether the first objectis an object of interest by prompting a user to verify that the firstobject indicates the object of interest, performing a featurecomparison, or a combination thereof, wherein performing the featurecomparison comprises comparing a location of the first subset of themultiple features relative to a central axis of the image sensor, adimension of the first subset of the multiple features relative to adimension of a second field of view, or a combination thereof; and basedon a determination that the first object is the object of interest,limit a field of view of the image sensor to the first object andexclude the second object.
 5. The electronic device of claim 4, whereinthe processor is to cause a display device to display the limited fieldof view of the image sensor.
 6. The electronic device of claim 4,wherein the processor is to cause a wireless transceiver to transmit avideo signal comprising the limited field of view of the image sensor.7. The electronic device of claim 4, wherein, to determine whether thefirst object is the object of interest, the processor is to perform afeature comparison by comparing a location of the first object to alocation of the second object, a dimension of the first object to adimension of the second object, the location of the first objectrelative to a boundary to the location of the second object relative tothe boundary, or a combination thereof.
 8. The electronic device ofclaim 4, wherein, to determine whether the first object is the object ofinterest, the processor is to perform a frame-by-frame featurecomparison by determining whether the first object, the second object,or a combination thereof is changing positions between the frame and asubsequent sequential frame.
 9. A non-transitory machine-readable mediumstoring machine-readable instructions which, when executed by aprocessor of an electronic device, cause the processor to: detect a usergesture in a video signal received via an image sensor; determine thatthe user gesture indicates that the video signal comprises an object ofinterest; detect multiple features of a frame of the video signal;determine, utilizing a machine learning technique, a subset of featuresof the multiple features that indicates the object of interest byprompting a user to verify that the subset of features indicates theobject of interest, performing a feature comparison, or a combinationthereof, wherein performing the feature comparison comprises comparing alocation of the subset of features relative to a central axis of theimage sensor, a dimension of the subset of features relative to adimension of a second field of view, or a combination thereof; and limita field of view of the image sensor to the subset of features.
 10. Thenon-transitory machine-readable medium of claim 9, wherein the processoris to determine that the user gesture indicates that the video signalcomprises the object of interest utilizing the machine learningtechnique.
 11. The non-transitory machine-readable medium of claim 9,wherein the processor is to determine, utilizing the machine learningtechnique, that a second subset of features of the multiple featuresindicates a background object.
 12. The non-transitory machine-readablemedium of claim 9, wherein the processor is to perform post-processingtechniques to reorient the limited field of view.
 13. The non-transitorymachine-readable medium of claim 12, wherein the post-processingtechniques include warping a perspective, adjusting a resolution,removing a background, or a combination thereof.